Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/33636
Title: Matrizes socioeconômicas no ajuste de modelos STARMA aplicados a dados epidemiológicos
Other Titles: Socioeconomic matrices in adjustment of STARMA models applied to epidemiological data
Authors: Sáfadi, Thelma
Sáfadi, Thelma
Lima, Renato Ribeiro de
Quimarães, Paulo Henrique Sales
Silva, Alessandra Querino da
Keywords: Matriz de vizinhança socioeconômica
Incidência de tuberculose
Starma
Socioeconomic neighborhood matrix
Tuberculosis
Incidence of tuberculosis
Issue Date: 22-Apr-2019
Publisher: Universidade Federal de Lavras
Citation: FREITAS, M. F. et al. Matrizes socioeconômicas no ajuste de modelos STARMA aplicados a dados epidemiológicos. 2019. 82 p. Dissertação (Mestrado em Estatística e Experimentação Agropecuária)-Universidade Federal de Lavras, Lavras, 2019.
Abstract: In this work the use of socioeconomic neighborhood matrices was studied in time-space models of autorregressive and moving averages (STARMA) class. The selected data set is composed of nine time series that quantify the incidence rate of Tuberculosis observed between 2002 and 2017 in the following cities: Belo Horizonte, Betim, Contagem, Governador Valadares, Juiz de Fora, Lavras, Montes Claros, Pouso Alegre and Uberlândia. Since most cities are geographically distant, the use of socioeconomic neighborhood matrices was necessary. The matrices were obtained through two socioeconomic variables: the municipal IDH and the average annual investment in basic health. The model was obtained computationally and consisted of three stages: Identification, estimation and diagnosis of the model. It was concluded that, contrary to the imagined, it is possible to observe the existence of space-time autocorrelation in the incidence rate of tuberculosis, even in cities that are geographically distant. The distance between the areas observed in this work has made the socio-economic neighborhood matrices become the most appropriate option in the adjustment of STARMA models to the data used in this work.
URI: http://repositorio.ufla.br/jspui/handle/1/33636
Appears in Collections:Estatística e Experimentação Agropecuária - Mestrado (Dissertações)



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